Various distortions, like atmospheric turbulence, degrade multimedia content such as images and videos by causing non-uniform geometric deformations and distortions due to random fluctuations of refractive index over air media. Atmospheric turbulence is a random phenomenon that distorts an imaging scene non-uniformly. The major distortions include geometric deformation and space-time varying blur. Mitigation of all such distortions and deformations in videos is a critical challenge.
Various techniques, such as, multi-frame image reconstruction and image averaging, have been implemented to mitigate such distortions. However, these techniques suit mostly for images and have limited scope when dealing with videos. Most of the video reconstruction procedures rely heavily on image registration techniques which are often computationally expensive.
Further, a technique based on iterative image registration has also been implemented to stabilize a sequence of frames and robust Principle Component Analysis (PCA) to eliminate sparse noise that remains after iterative registration. In any distorted or corrupted image or frame, low dimensional structure is hidden. The PCA is typically used to exploit lower dimensional embeddings from higher dimensional data. The PCA based approach has been successfully validated for data de-correlation and data dimensionality reduction in image and video processing applications. The major disadvantages with PCA based method are its sensitivity to outliers and gross data corruption. The iterative image registration and robust PCA based method removes water wave turbulence present in underwater scenes and can also be applied to air turbulence since both water and air turbulence are having nearly same effects with more blurring than warping in later case. This method has certain disadvantages like high computational cost due to iterative image registration procedure and no moving object detection capability. In addition, this method is sensitive to blur estimation stage in the sense that unnecessary blur may be introduced in reconstructed result if blur is wrongly estimated.
Thereafter, an approach to mitigate the turbulence and simultaneous detection of moving object was introduced. The approach included separating the input image into its constituent turbulence, foreground, and background images using matrix decomposition. A separate norm to minimize each component (e.g., turbulence, foreground and background images) is used to achieve the desired low rank matrix decomposition. The major disadvantage of this method is that, it can only detect slow moving objects, because it employs temporal image averaging as a pre-processing stage (which creates multiple blur footprints of fast moving objects) to remove most of the turbulence. Further, this method is slow and may be treated offline since it requires time to compute object confidence map using intensity and a motion model, which is necessary for moving object detection. Furthermore, this method causes increased blur due to image averaging.
Therefore, there exists a need for a fast and real-time technique for mitigating distortions in a multimedia content.